The color histograms, GIST, and texture descriptors are global features that represent certain statistical characteristics of an image like color, texture [1]}, [2]}, and spatial structure [3]}. While HOG and SIFT are local features that describe geometrical information. Usually they are used to construct bag-of-visual-words (BoVW) models [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]} and HOG feature-based models [14]}, [15]}. Some popular feature encoding or pooling strategies to enhance the performance of BoVW are Fisher vector coding [16]}, [17]}, [18]}, Spatial Pyramid Matching (SPM) [19]}, and Probabilistic Topic Model (PTM) [20]}, [21]}, [22]}, [13]}. A single feature is insufficient to represent the whole image information, hence a combination of these features is used for image classification [24]}, [5]}, [6]}, [20]}, [22]}, [29]}, [30]}, [31]}, [32]}, [33]}, [34]}.
